token-holder-analysis

Name: token-holder-analysis
Author: agiprolabs

235stars53forks

How to install token-holder-analysis?

agiprolabs/claude-trading-skills/token-holder-analysis

$npx -y skills add agiprolabs/claude-trading-skills --skill token-holder-analysis

Installs into the current project.

Summary

Token holder distribution, concentration metrics, insider detection, and supply analysis for Solana tokens

Files · 1

View on GitHub

SKILL.md

1	# Token Holder Analysis — Concentration, Distribution & Risk
2
3	Analyze who holds a token, how concentrated ownership is, and whether insider patterns suggest risk. This is a critical pre-trade safety check — high concentration means a few wallets can crash the price.
4
5	## Quick Start
6
7	```python
8	import httpx
9	import math
10
11	# Using Helius DAS API for holder data
12	HELIUS_KEY = os.getenv("HELIUS_API_KEY", "")
13	HELIUS = f"https://mainnet.helius-rpc.com/?api-key={HELIUS_KEY}"
14
15	# Or using SolanaTracker for holder + risk data
16	ST_KEY = os.getenv("SOLANATRACKER_API_KEY", "")
17	ST = "https://data.solanatracker.io"
18
19	# Get top holders via RPC
20	def get_top_holders(mint: str) -> list[dict]:
21	resp = httpx.post(HELIUS, json={
22	"jsonrpc": "2.0", "id": 1,
23	"method": "getTokenLargestAccounts",
24	"params": [mint],
25	})
26	return resp.json()["result"]["value"]
27
28	holders = get_top_holders("TOKEN_MINT")
29	```
30
31	## Data Sources
32
33	\| Source \| What It Provides \| Auth \|
34	\|--------\|-----------------\|------\|
35	\| Solana RPC (`getTokenLargestAccounts`) \| Top 20 holders, supply \| RPC key \|
36	\| Helius DAS (`getAsset`, token accounts) \| Parsed holder data, metadata \| API key \|
37	\| SolanaTracker (`/tokens/{t}/holders/top`) \| Top 100 holders, bundler detection \| API key \|
38	\| Birdeye (`/defi/token_security`) \| Top 10 %, creator balance, freeze/mint auth \| API key \|
39
40	## Concentration Metrics
41
42	### Top-N Holder Percentage
43
44	The simplest measure — what % of supply do the top N holders control?
45
46	```python
47	def top_n_percentage(holders: list[dict], supply: int, n: int = 10) -> float:
48	"""Calculate percentage held by top N holders.
49
50	Args:
51	holders: Sorted list of holders (largest first).
52	supply: Total token supply.
53	n: Number of top holders.
54
55	Returns:
56	Percentage (0-100) held by top N.
57	"""
58	top_n_amount = sum(int(h.get("amount", 0)) for h in holders[:n])
59	return top_n_amount / supply * 100 if supply > 0 else 0
60	```
61
62	Risk thresholds:
63	- Top 10 < 30%: Well distributed
64	- Top 10 30-50%: Moderate concentration
65	- Top 10 50-80%: High concentration — significant dump risk
66	- Top 10 > 80%: Extreme — likely controlled by a few wallets
67
68	### Gini Coefficient
69
70	Measures inequality of token distribution (0 = perfectly equal, 1 = one holder owns everything).
71
72	```python
73	def gini_coefficient(amounts: list[float]) -> float:
74	"""Calculate Gini coefficient for holder distribution.
75
76	Args:
77	amounts: List of holder amounts (any order).
78
79	Returns:
80	Gini coefficient between 0 and 1.
81	"""
82	if not amounts or all(a == 0 for a in amounts):
83	return 0.0
84	sorted_amounts = sorted(amounts)
85	n = len(sorted_amounts)
86	cumsum = sum((i + 1) * a for i, a in enumerate(sorted_amounts))
87	total = sum(sorted_amounts)
88	return (2 * cumsum) / (n * total) - (n + 1) / n
89	```
90
91	Interpretation for crypto tokens:
92	- Gini < 0.6: Unusual, very well distributed
93	- Gini 0.6-0.8: Typical for established tokens
94	- Gini 0.8-0.95: Common for newer tokens
95	- Gini > 0.95: Extreme concentration, high risk
96
97	### Herfindahl-Hirschman Index (HHI)
98
99	Measures market concentration — sum of squared market shares.
100
101	```python
102	def hhi(amounts: list[float]) -> float:
103	"""Calculate HHI for holder concentration.
104
105	Args:
106	amounts: List of holder amounts.
107
108	Returns:
109	HHI value (0-10000). Higher = more concentrated.
110	"""
111	total = sum(amounts)
112	if total == 0:
113	return 0.0
114	shares = [a / total * 100 for a in amounts]
115	return sum(s ** 2 for s in shares)
116	```
117
118	Interpretation:
119	- HHI < 1500: Competitive (unconcentrated)
120	- HHI 1500-2500: Moderately concentrated
121	- HHI > 2500: Highly concentrated
122
123	### Nakamoto Coefficient
124
125	Minimum number of holders needed to control >50% of supply.
126
127	```python
128	def nakamoto_coefficient(amounts: list[float]) -> int:
129	"""Calculate Nakamoto coefficient (holders needed for 51%).
130
131	Args:
132	amounts: Sorted list of holder amounts (largest first).
133
134	Returns:
135	Number of holders needed for majority control.
136	"""
137	total = sum(amounts)
138	if total == 0:
139	return 0
140	threshold = total * 0.51
141	cumulative = 0
142	for i, amount in enumerate(sorted(amounts, reverse=True)):
143	cumulative += amount
144	if cumulative >= threshold:
145	return i + 1
146	return len(amounts)
147	```
148
149	## Insider Detection Patterns
150
151	### Bundler Detection
152
153	Bundlers use atomic transaction bundles (via Jito) to execute coordinated buys at token launch. Detection signals:
154
155	```python
156	def detect_bundler_patterns(holders: list[dict], first_buyers: list[dict]) -> dict:
157	"""Identify potential bundler activity.
158
159	Args:
160	holders: Current top holders.
161	first_buyers: Early buyers from SolanaTracker /first-buyers endpoint.
162
163	Returns:
164	Bundler risk analysis.
165	"""

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token-holder-analysis

235stars53forks

View on GitHub

How to install token-holder-analysis?

agiprolabs/claude-trading-skills/token-holder-analysis

$npx -y skills add agiprolabs/claude-trading-skills --skill token-holder-analysis

Installs into the current project.

Summary

Token holder distribution, concentration metrics, insider detection, and supply analysis for Solana tokens

Files · 1

View on GitHub

SKILL.md

1	# Token Holder Analysis — Concentration, Distribution & Risk
2
3	Analyze who holds a token, how concentrated ownership is, and whether insider patterns suggest risk. This is a critical pre-trade safety check — high concentration means a few wallets can crash the price.
4
5	## Quick Start
6
7	```python
8	import httpx
9	import math
10
11	# Using Helius DAS API for holder data
12	HELIUS_KEY = os.getenv("HELIUS_API_KEY", "")
13	HELIUS = f"https://mainnet.helius-rpc.com/?api-key={HELIUS_KEY}"
14
15	# Or using SolanaTracker for holder + risk data
16	ST_KEY = os.getenv("SOLANATRACKER_API_KEY", "")
17	ST = "https://data.solanatracker.io"
18
19	# Get top holders via RPC
20	def get_top_holders(mint: str) -> list[dict]:
21	resp = httpx.post(HELIUS, json={
22	"jsonrpc": "2.0", "id": 1,
23	"method": "getTokenLargestAccounts",
24	"params": [mint],
25	})
26	return resp.json()["result"]["value"]
27
28	holders = get_top_holders("TOKEN_MINT")
29	```
30
31	## Data Sources
32
33	\| Source \| What It Provides \| Auth \|
34	\|--------\|-----------------\|------\|
35	\| Solana RPC (`getTokenLargestAccounts`) \| Top 20 holders, supply \| RPC key \|
36	\| Helius DAS (`getAsset`, token accounts) \| Parsed holder data, metadata \| API key \|
37	\| SolanaTracker (`/tokens/{t}/holders/top`) \| Top 100 holders, bundler detection \| API key \|
38	\| Birdeye (`/defi/token_security`) \| Top 10 %, creator balance, freeze/mint auth \| API key \|
39
40	## Concentration Metrics
41
42	### Top-N Holder Percentage
43
44	The simplest measure — what % of supply do the top N holders control?
45
46	```python
47	def top_n_percentage(holders: list[dict], supply: int, n: int = 10) -> float:
48	"""Calculate percentage held by top N holders.
49
50	Args:
51	holders: Sorted list of holders (largest first).
52	supply: Total token supply.
53	n: Number of top holders.
54
55	Returns:
56	Percentage (0-100) held by top N.
57	"""
58	top_n_amount = sum(int(h.get("amount", 0)) for h in holders[:n])
59	return top_n_amount / supply * 100 if supply > 0 else 0
60	```
61
62	Risk thresholds:
63	- Top 10 < 30%: Well distributed
64	- Top 10 30-50%: Moderate concentration
65	- Top 10 50-80%: High concentration — significant dump risk
66	- Top 10 > 80%: Extreme — likely controlled by a few wallets
67
68	### Gini Coefficient
69
70	Measures inequality of token distribution (0 = perfectly equal, 1 = one holder owns everything).
71
72	```python
73	def gini_coefficient(amounts: list[float]) -> float:
74	"""Calculate Gini coefficient for holder distribution.
75
76	Args:
77	amounts: List of holder amounts (any order).
78
79	Returns:
80	Gini coefficient between 0 and 1.
81	"""
82	if not amounts or all(a == 0 for a in amounts):
83	return 0.0
84	sorted_amounts = sorted(amounts)
85	n = len(sorted_amounts)
86	cumsum = sum((i + 1) * a for i, a in enumerate(sorted_amounts))
87	total = sum(sorted_amounts)
88	return (2 * cumsum) / (n * total) - (n + 1) / n
89	```
90
91	Interpretation for crypto tokens:
92	- Gini < 0.6: Unusual, very well distributed
93	- Gini 0.6-0.8: Typical for established tokens
94	- Gini 0.8-0.95: Common for newer tokens
95	- Gini > 0.95: Extreme concentration, high risk
96
97	### Herfindahl-Hirschman Index (HHI)
98
99	Measures market concentration — sum of squared market shares.
100
101	```python
102	def hhi(amounts: list[float]) -> float:
103	"""Calculate HHI for holder concentration.
104
105	Args:
106	amounts: List of holder amounts.
107
108	Returns:
109	HHI value (0-10000). Higher = more concentrated.
110	"""
111	total = sum(amounts)
112	if total == 0:
113	return 0.0
114	shares = [a / total * 100 for a in amounts]
115	return sum(s ** 2 for s in shares)
116	```
117
118	Interpretation:
119	- HHI < 1500: Competitive (unconcentrated)
120	- HHI 1500-2500: Moderately concentrated
121	- HHI > 2500: Highly concentrated
122
123	### Nakamoto Coefficient
124
125	Minimum number of holders needed to control >50% of supply.
126
127	```python
128	def nakamoto_coefficient(amounts: list[float]) -> int:
129	"""Calculate Nakamoto coefficient (holders needed for 51%).
130
131	Args:
132	amounts: Sorted list of holder amounts (largest first).
133
134	Returns:
135	Number of holders needed for majority control.
136	"""
137	total = sum(amounts)
138	if total == 0:
139	return 0
140	threshold = total * 0.51
141	cumulative = 0
142	for i, amount in enumerate(sorted(amounts, reverse=True)):
143	cumulative += amount
144	if cumulative >= threshold:
145	return i + 1
146	return len(amounts)
147	```
148
149	## Insider Detection Patterns
150
151	### Bundler Detection
152
153	Bundlers use atomic transaction bundles (via Jito) to execute coordinated buys at token launch. Detection signals:
154
155	```python
156	def detect_bundler_patterns(holders: list[dict], first_buyers: list[dict]) -> dict:
157	"""Identify potential bundler activity.
158
159	Args:
160	holders: Current top holders.
161	first_buyers: Early buyers from SolanaTracker /first-buyers endpoint.
162
163	Returns:
164	Bundler risk analysis.
165	"""

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